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A network clustering based feature selection strategy for classifying autism spectrum disorder

Authors
  • Tang, Lingkai1
  • Mostafa, Sakib2
  • Liao, Bo3
  • Wu, Fang-Xiang1, 2
  • 1 Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9, Canada , Saskatoon (Canada)
  • 2 Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9, Canada , Saskatoon (Canada)
  • 3 School of Mathematics and Statistics, Hainan Normal University, Haikou, 571158, China , Haikou (China)
Type
Published Article
Journal
BMC Medical Genomics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Dec 30, 2019
Volume
12
Issue
Suppl 7
Identifiers
DOI: 10.1186/s12920-019-0598-0
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundAdvanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance.MethodsIn this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification.ResultsThe computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network.ConclusionIt is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

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